↓ Skip to main content

The impact of health information technology on disparity of process of care

Overview of attention for article published in International Journal for Equity in Health, April 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
8 X users

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
73 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The impact of health information technology on disparity of process of care
Published in
International Journal for Equity in Health, April 2015
DOI 10.1186/s12939-015-0161-3
Pubmed ID
Authors

Jinhyung Lee

Abstract

Disparities in the quality of health care and treatment among racial or ethnic groups can result from unequal access to medical care, disparate treatments for similar severities of symptoms, and wide divergence in general health status among individuals. Such disparities may be eliminated through better use of health information technology (IT). Investment in health IT could foster better coordinated care, improve guideline compliance, and reduce the likelihood of redundant testing, thereby encouraging more equitable treatment for underprivileged populations. However, there is little research exploring the impact of health IT investment on disparities of process of care. This study examines the impact of health IT investment on waiting times - from admission to the date of first principle procedure - among different racial and ethnic groups, using patient and hospital data for the state of California collected from 2001 to 2007. The final sample includes 14,056,930 patients admitted with medical diseases to 316 unique, acute-care hospitals over a seven-year period. The linear random intercept and slope model was employed to examine the impacts of health IT investment on waiting time, while controlling for patient, disease, and hospital characteristics. Greater health IT investment was associated with shorter waiting times, and the reduction in waiting times was greater for non-White than for White patients. This indicates that minority populations could benefit from health IT investment with regard to process of care. Investments in health IT may reduce disparities in process of care.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 11%
Other 6 8%
Researcher 6 8%
Student > Bachelor 6 8%
Student > Doctoral Student 4 5%
Other 14 19%
Unknown 29 40%
Readers by discipline Count As %
Medicine and Dentistry 13 18%
Nursing and Health Professions 8 11%
Social Sciences 7 10%
Business, Management and Accounting 4 5%
Engineering 3 4%
Other 6 8%
Unknown 32 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 July 2018.
All research outputs
#2,633,593
of 22,800,560 outputs
Outputs from International Journal for Equity in Health
#470
of 1,899 outputs
Outputs of similar age
#35,716
of 264,674 outputs
Outputs of similar age from International Journal for Equity in Health
#3
of 14 outputs
Altmetric has tracked 22,800,560 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,899 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.2. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,674 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.